Introducing functional programming in the Haskell language, this book is written for students and programmers with little or no experience. It emphasises the process of crafting programmes, problem solving and avoiding common programming pitfalls. Covering basic functional programming, and abstraction to larger scale programming.

Functional programming mainly sees use in math computations, including those used in Artificial Intelligence and gaming. This programming paradigm makes algorithms used for math calculations easier to understand and provides a concise method of coding algorithms by people who aren't developers.

Learn how functional programming can help you in deploying web servers and working with databases in a declarative and pure way

Summary Programming with Types teaches you to design safe, resilient, correct software that's easy to maintain and understand by taking advantage of the power of strong type systems. Designed to provide practical, instantly useful techniques for working developers, this clearly written tutorial introduces you to using type systems to support everyday programming tasks.

Software Design for Engineers and Scientists integrates three core areas of computing: . Software engineering - including both traditional methods and the insights of 'extreme programming' . Program design - including the analysis of data structures and algorithms .

Applied Evolutionary Algorithms for Engineers with Python is written for students, scientists and engineers who need to apply evolutionary algorithms to practical optimization problems.

Computational intelligence is a general term for a class of algorithms designed by nature's wisdom and human intelligence. Computer scientists have proposed many computational intelligence algorithms with heuristic features.

Edited by professionals with years of experience, this book provides an introduction to the theory of evolutionary algorithms and single- and multi-objective optimization, and then goes on to discuss to explore applications of evolutionary algorithms for many uses with real-world applications.

This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning.

Data Structures is a central module in the curriculum of almost every Computer Science programme. This book explains different concepts of data structures using C. The topics discuss the theoretical basis of data structures as well as their applied aspects.

This books reviews recent theoretical and practical advances on suffix sorting and introduces algorithmic solutions to problems of wide interest for the construction of fundamental data structures that operate efficiently on strings

Understand and solve complex computational problems and write efficient code with Kotlin Key Features. Learn about important data structures such as lists, arrays, queues, and stacks. Design custom algorithms for real-life implementations.

The data structure is a set of specially organized data elements and functions, which are defined to store, retrieve, remove and search for individual data elements. Data Structures using C: A Practical Approach for Beginners covers all issues related to the amount of storage needed, the amount of time required to process the data, data representation of the primary memory and operations carried out with such data.

Data structures and algorithms is a fundamental course in Computer Science, which enables learners across any discipline to develop the much-needed foundation of efficient programming, leading to better problem solving in their respective disciplines.

As there can be more than one algorithm for the same problem, designing and analyzing an algorithm becomes important in order to make it as efficient and robust as possible. This book will serve as a guide to design and analysis of computer algorithms.

Includes complete coverage of basics and design of algorithms Discusses algorithm analysis techniques like divide-and-conquer, dynamic programming, and greedy heuristics Provides time and space complexity tutorials Reviews combinatorial optimization of Knapsack problem Simplifies recurrence relation for time complexity

Includes - Chapter 8: Reusing Code with Many Different Types -- Polymorphism in Reason -- Generic functions with parametric polymorphism -- Ad hoc polymorphism or overloading -- Generic code with functors -- What is a functor? -- Example 1 - looking in the standard library -- Example 2 -- Example 3 -- Summary -- Chapter 9: Extending Types with New Behavior -- Subtyping using polymorphic variants -- Reusing constructors for different types -- An example of polymorphic variant type extension -- More about extending polymorphic variant types -

Includes - Chapter 1: Functional Patterns - the Building Blocks; Higher-order functions; Functions as first-class citizens; Composing functions; Currying functions; Currying and composability; Decoupling with currying; Recursion; Non-tail recursion; Tail recursion; Folding abstracts recursion; Types, pattern matching, and polymorphism; Algebraic types and pattern matching; Recursive types; Polymorphism; Parametric polymorphism; Ad-hoc polymorphism; Functions, types, and patterns